New generation communication networks are moving towards autonomous wireless infrastructures which are very popular in the application of multimedia broadcasting and mobile communication where N numbers of data are transfer through the wireless network every day. In such applications security of transmitted signal is very important in wireless communication network. So the proposed work creates a methodology to increase the security of the data and communication using chaotic encryption algorithm to transfer the data from the wireless network. A proposed new structure is based on coupling of chaotic system. We combine the text message with the chaotic signals to reduce the attack and improve the security of the data. The performance of BER in AWGN channel are verified and analyzed with MATLAB toolbox.

1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 07, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 507
A Secure Chaotic Communication System
Mr. Pravin Mawale1
Ms. Vrushali Shirpurkar2
Prof. Gouri Halde3
Prof. Sony Chaturvedi4
3,4
Professor
1,2,3,4
Department of Electronics and Communication Engineering
1,2,3,4
P.I.E.T. R.T.M. Nagpur University
Abstract— New generation communication networks are
moving towards autonomous wireless infrastructures which
are very popular in the application of multimedia
broadcasting and mobile communication where N numbers
of data are transfer through the wireless network every day.
In such applications security of transmitted signal is very
important in wireless communication network. So the
proposed work creates a methodology to increase the
security of the data and communication using chaotic
encryption algorithm to transfer the data from the wireless
network. A proposed new structure is based on coupling of
chaotic system. We combine the text message with the
chaotic signals to reduce the attack and improve the security
of the data. The performance of BER in AWGN channel are
verified and analyzed with MATLAB toolbox.
Keywords: Wireless Network, Chaotic algorithm,
Encryption, Decryption, BER
I. INTRODUCTION
The field of chaotic communications has gone through
various periods of intense interest, initiated by Shannon’s
1947 recognition that the channel capacity of a
communications link is optimized when the waveform is a
noise-like maximal entropy signal [1] and further solidified
by Chua’s 1980 implementation of a practical chaotic
electrical circuit [2]. Chaotic communication systems
resemble direct sequence spread spectrum communication
systems in that the data is spread across a relatively wide
transmission bandwidth and then de spread by the intended
receiver with a time-synchronized spreading sequence.
These systems tend to be more computationally complex
than non-spread communication systems, yet they provide
advantageous multipath mitigation and multi-user spectral
re-use capabilities. The chaotic sequence based
communication systems exhibit analytically better
performance than direct sequence based communication
systems and may in general be viewed as a generalization of
direct sequence approaches; as its layman’s connotation
would suggest, a chaotic sequence or system evolves in a
seemingly random fashion, while the direct sequence system
is limited to a small finite set of values. The most significant
limitation of chaotic communication systems is the extreme
precision needed to accurately synchronize and track two
independent instantiations of an “identical” chaotic circuit as
used at a transmitter and a receiver. Active research in
chaotic communications was revived in the early 1990s
when various chaotic circuit synchronization methods were
demonstrated, leading to limited communications
capabilities; each of the proposed methods had drawbacks
that limited their practical implementations. During this
period, the theoretical performance of chaotic
communications has been shown by various authors
[3][4][5][6] to exceed that of direct sequence systems and,
ultimately, to satisfy Shannon’s noise-like waveform
characteristics for a maximal entropy waveform that
maximizes channel capacity. To date, nobody has
demonstrated a sufficiently robust chaotic circuit
synchronization method that supports practical coherent
chaotic communications with direct sequence approaches.
The proposed work presents a divergence from the
traditional chaotic communications approaches that harness
analog chaotic circuits by implementing a fully digital
chaotic circuit that is then conditioned for use in a prototype
coherent chaotic communications system. Topics include a
comparison of analog and digital chaotic circuits;
implementation of digital chaotic circuits for use in chaotic
communications; analytical, simulation, and measured
hardware results for a prototype coherent communication
system; and generalization of the fundamental chaotic
waveform to multipath mitigation techniques, multiple
access communication systems, permission-based
communication systems, and a new class of maximal
entropy amplitude modulated chaotic waveform hybrids for
use in specific applications.
II. FORMULATION OF CHAOTIC SYSTEMS
The Lorenz system is a system of ordinary differential
equations (the Lorenz equations) first studied by Edward
Lorenz. It is notable for having chaotic solutions for certain
parameter values and initial conditions. In particular, the
Lorenz attractor is a set of chaotic solutions of the Lorenz
system which, when plotted, resemble a butterfly or figure
eight.
A general chaotic system can be described as the
following dynamic equation:
̇ ( ) (1)
Where Ax is the linear part, g(x) is the nonlinear
part of this system. In this paper, the chaotic security system
is constructed by using Lorenz’s chaotic system which is an
autonomous 3-order nonlinear system. Its dynamic states are
presented as following:
ẋ = dx/dt=s*(y-x)
ẏ = dy/dt=r*x-y-xz (2)
z = dz/dt=x*y-b*z;
Where x, y, and z are the dynamic states, r, s, and b
are constants greater than zero. Fig. 1 shows the attractor of
Eq. (2) with the parameters of r=28, s=10, b=8/3 and initial
state of [x0, y0, z0] =[0.1, 0.2 , 0.3].
The proposed scheme can be distributed
by a coupled chaotic system. The input message
signal M is masked by the chaotic state
variables and transmitted. The equations of
encryption for transmitter and decryption for
receiver systems are mentioned as follows:

2. A Secure Chaotic Communication System
(IJSRD/Vol. 2/Issue 07/2014/111)
All rights reserved by www.ijsrd.com 508
Encrypt side (transmitter): .ẋ Ax g(x,v)
Lzx. (3)
where v x1 M
Decrypt side (receiver): y Ay g(y,v) Lzy.
(4)
Fig. 1: The Lorenz chaotic attractor
where x €Rn , y €Rn are the state
vectors. Ax and Ay are the linear part the
system, g(x,v) and g( y,v) are the nonlinear
part of the system, L is the controller gain of
the system, K>0 is the coupling strength between
master and slave system, zx and zy are the
feedback signal.
From Eqs. (3) and (4), at wired chaotic security
system, the encrypted message and decrypted message are
shown in Fig. 2, respectively. The parameters are set at
r=28, s=10, b=8/3, h=0.01, l1=0, l2=38, l3=0. The initial
states of transmeter are [x10,x20,x30]=[0.1,0,0] and states of
slave are [y10,y20,y30]=[0.15,0,0].InFig.2,the decrypted
message is identical to encrypted one. However, in
environment like wireless, the decrypted message is
critically damaged as shown in Fig. 3 due to the propagation
delay, uncertainty signal fading and so on. Furthermore, the
Gaussian distribution noise will be added into the
transmitted chaotic signal and damaged its primal state in
AWGN (Additive White Gaussian Noise) channel.
Fig. 2: for perfectly decrepted data
Fig. 3: cryticaly damaged decrypt message
III. PROPOSED ALGORITHM
Following is the outline for proposed text based Chaotic
Communication algorithm.
(1) Take a input text message.
(2) Apply chaotic encryption algorithm for encoding of
the text document.
(3) Transmit encrypted data from different n number of
transmitter channel.
(4) Formation of NF Coupling between transmitter and
receivers.
(5) Receiving data from different n number of
receivers.
(6) Calculating BER (Bit Error Rate).
(7) Combining different outputs from all receivers and
finding its threshold value.
(8) Performing chaotic decryption algorithm on output.
IV. BIT ERROR RATE
In digital transmission, the number of bit errors is the
number of received bits of a data stream over a
communication channel that has been altered due to noise,
interference, distortion or bit synchronization errors. The bit
error rate or bit error ratio (BER) is the number of bit errors
divided by the total number of transferred bits during a
studied time interval. BER is a unit less performance
measure, often expressed as a percentage. The bit error
probability pe is the expectation value of the BER. The BER
can be considered as an approximate estimate of the bit error
probability. This estimate is accurate for a long time interval
and a high number of bit errors.
Example:
As an example, assume this transmitted bit sequence:
0 1 1 0 0 0 1 0 1 1,
and the following received bit sequence:
0 0 1 0 1 0 1 0 0 1,
The number of bit errors (the underlined bits) is in
this case 3. The BER is 3 incorrect bits divided by 10
transferred bits, resulting in a BER of 0.3 or 30%.
V. FLOWCHART OF PROPOSED METHOD
Lets see the details of flow chart which is shown in figure 4
Step 1: Take an input text message want to transmit
Take the text message you want to transmit through the
communication network. In message stream words are
converted according to their ASCII values and the those
ASCII numbers are converted into binary number for the
transmission through the network.
Step 2: Appling Chaotic communication
encryption algorithm:
Applying the chaotic encryption algorithm for breaking the
text message in number of data packets in Encrypted form.

3. A Secure Chaotic Communication System
(IJSRD/Vol. 2/Issue 07/2014/111)
All rights reserved by www.ijsrd.com 509
Step 3: Transmit encrypted data
Transmit the encrypted data packets from the different n
number of transmitters.
Step 4: Receive text message packets
Receive the encrypted data through the different number
Receivers
Step 5: Calculating BRR (Bit Error Rate) and combining
the data:
Fig. 4: Flow chart of the proposed Text Based Chaotic
Communication
Now calculate the Bit Error Rate for finding the
number of data loses during the transmission of data through
the network. Now all the data receive from the different
receivers are combining to get the original transmitted
message transmit from the source.
Simulation Table and Graphs
Coupling Data Length Noise Level Variance
1 36 10 0.06
1 36 15 0.243
1 36 25 0.32
2 36 10 0.03
2 36 15 0.111
2 36 25 0.227
3 36 10 0.015
3 36 15 0.055
3 36 25 0.113
4 36 10 0.008
4 36 15 0.027
4 36 25 0.056
5 36 10 0.003
5 36 15 0.013
5 36 25 0.028
Table 2: Simulation Result table
Table 1 shows that when we increase the coupling
level the value of variance in reduced continuously.
Graph 1: Simulation result against coupling verses variance
Fig. 5: Simulation Result for coupling 1 and noise level 10
with lorentz
Fig. 6: Simulation Result for coupling 1 and noise level 25
without lorentz
Fig. 7: Simulation Result for coupling 3 and noise level 25
with lorentz
Fig. 8: Simulation Result for coupling 3 and noise level 25
without lorentz
0
0.2
0.4
111 222 333 444 555
Variance
Variance
Rx
1
Tx
1
Combini
ng Msg.
Rx
2
Tx
2
Finding
BER
Input
Text
Finding
Threshol
d Value
Tx
n
Rxn
Output
Messag
e

4. A Secure Chaotic Communication System
(IJSRD/Vol. 2/Issue 07/2014/111)
All rights reserved by www.ijsrd.com 510
VI. CONCLUSION
The proposed work presents the methodology to secure the
wireless communication. For this we used the chaotic
communication system. For transferring the data from one
place to another we first used encryption method and break
the data into n number of packets by using chaotic
encryption mechanism. We add the data packets with
chaotic signals to increase the security of data transfer in the
communication network. The proposed work gives the
comparison of transmission of data with and without chaotic
signals and we observed that the bit error rate of the
transmitted data is reduced with improved security using
chaotic communication. Results graph shows that we are
able to transfer the data with the noise and received it on the
receiver side successfully. In the simulation results, the
relativity BER and number of Encrypt/Decrypt system
analysis is discussed.
REFERENCES
[1] C. Shannon, “Communication in the presence of
noise,” Proc. Inst. Radio Eng., vol. 37, pp. 10–21, Jan
2011.
[2] L. Chua, “Dynamic nonlinear networks: State-of-the-
art,” IEEE Transactions on Circuits and Systems,vol.
27, pp. 1059–1087, Nov 1980.
[3] H. Dedieu, M. Kennedy, and M. Hasler, “Chaos shift
keying: modulation and demodulation of a
chaoticcarrier using self-synchronizing chua’s
circuits,” IEEE Transactions on Circuits and Systems
II, vol. 40,pp. 634–642, 1993.
[4] R. Pickholtz, D. Schilling, and L. Milstein, “Theory
of spread spectrum communications – a tutorial,”
IEEE Transactions on Communications, vol. 30, pp.
855–884, May 2012.
[5] G. Kolumban, M. Kennedy, and L. Chua, “The role
of synchronization in digital communications
usingchaos: Fundamentals of digital
communications,” IEEE Transactions on Circuits and
Systems I: Funda-mental Theory and Applications,
vol. 44, no. 10, pp. 927–936, Oct 1997.
[6] M. Hasler and Y. Maistrenko, “An introduction to the
synchronization of chaotic systems: coupled skewtent
maps,” IEEE Transactions on Circuits and Systems I:
Fundamental Theory and Applications, vol. 44,no.
10, pp. 856–866, Oct 1997.
[7] R. Scholtz, “The origins of spread-spectrum
communications,” IEEE Transactions on
Communications,vol. 30, pp. 822–854, 2008.
[8] E. Appleton and M. Barnett, “On some direct
evidence for downward atmospheric reflection of
electricrays,” Proceedings Royal Society Series A,
vol. 109, pp. 621–641, Dec 2010.
[9] J. Boone and R. Peterson, “SIGSALY: the start of the
digital
revolution,”http://www.nsa.gov/publications/publi00
019.cfm, 23 Apr 2007.
[10]C. Shannon, “A mathematical theory of
communication,” Bell System Technical Journal, vol.
27, pp. 379–423, 623–656, 1948.
[11]C. Shannon, “Communication theory of secrecy
systems,” Bell System Technical Journal, vol. 28, pp.
656–715, Oct 1949.
[12]R. Pickholtz, L. Milstein, and D. Schilling, “Spread
spectrum for mobile communications,” IEEE Trans-
actions on Vehicular Technology, vol. 40, pp. 313–
322, May 1991.
[13]Celikovsk´y, S., and Vanˇeˇcek, A. Bilinear systems
and chaos.Kybernetika , 403–424.
[14]Vanˇeˇcek, A., and ˇ Celikovsk´y, S. Control systems:
from linear analysis to synthesis of chaos. Prentice-
Hall, London, 1996.
[15]Chen, G., and Li, Z. Global synchronization and
asymptotic stability of complex dynamical networks.
IEEE Trans. on Circ. Sys.–II 53 (2006), 28–33.
[16]Menezes, A. J., Vanstone, S. A., and Oorschot, P. C.
V.Handbook of Applied Cryptography. CRC Press,
Inc., Boca Raton, FL,USA, 1996.